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Updated: Jun 14, 2025

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Deciphering Factors Contributing to Cost-Effective Medicine Using Machine Learning.

Bowen Long1, Jinfeng Zhou2, Fangya Tan1

  • 1Department of Analytics, Harrisburg University of Science and Technology, Harrisburg, PA 17101, USA.

Bioengineering (Basel, Switzerland)
|August 29, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning identified key factors for over-the-counter (OTC) medication cost-effectiveness. Factors like FSA/HSA eligibility, symptom range, and packaging size significantly influence perceived value for consumers.

Keywords:
cost-effective medicinecost-effectiveness rating (CER)machine learning

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Area of Science:

  • Pharmacoeconomics
  • Health Informatics
  • Consumer Health

Background:

  • Over-the-counter (OTC) medications are widely used, but their cost-effectiveness is not always clear to consumers.
  • Identifying factors that influence the perceived value of OTC medications is crucial for informed purchasing decisions.
  • Existing research often overlooks the interplay between product attributes, pricing, and consumer financial accounts.

Purpose of the Study:

  • To identify critical factors influencing the cost-effectiveness of OTC medications using machine learning.
  • To develop a novel cost-effectiveness rating (CER) model incorporating user ratings and prices.
  • To analyze how specific medication characteristics impact cost-effectiveness across different therapeutic categories.

Main Methods:

  • Utilized machine learning algorithms to analyze a large dataset of OTC medications from Amazon.
  • Developed a proprietary cost-effectiveness rating (CER) metric based on user reviews and product pricing.
  • Examined the influence of factors including Flexible Spending Account (FSA)/Health Savings Account (HSA) eligibility, symptom treatment range, safety warnings, special effects, active ingredients, and packaging size.

Main Results:

  • FSA/HSA eligibility, broader symptom treatment range, and smaller packaging size were positively correlated with higher cost-effectiveness.
  • Cold medicines with safety warnings, featuring ingredients like phenylephrine and acetaminophen, demonstrated cost-effectiveness due to lower prices.
  • Allergy medications with child-friendly attributes and digestion medicines containing calcium, famotidine, or magnesium showed enhanced cost-effectiveness.

Conclusions:

  • Machine learning can effectively identify key drivers of OTC medication cost-effectiveness.
  • Consumer perception of value is influenced by a combination of financial accessibility (FSA/HSA), therapeutic breadth, and product attributes.
  • These findings provide actionable insights for consumers, manufacturers, and retailers to optimize OTC medication choices and market strategies.